mlsanityData to 14 Oct 2026

Geo-Supervised Visual Depth Prediction

arXiv 1807.11130 ↗First posted 30 Jul 2018cs.CV cs.ROcited by 33

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Abstract (arXiv)

We propose using global orientation from inertial measurements, and the bias it induces on the shape of objects populating the scene, to inform visual 3D reconstruction. We test the effect of using the resulting prior in depth prediction from a single image, where the normal vectors to surfaces of objects of certain classes tend to align with gravity or be orthogonal to it. Adding such a prior to baseline methods for monocular depth prediction yields improvements beyond the state-of-the-art and illustrates the power of gravity as a supervisory signal.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jul 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 9; 2023: 6; 2024: 5; 2025: 4; 2026: 3.

33 papers.

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  1. 2022Physics-Informed Machine Learning: A Survey on Problems, Methods and Applicationscited by 92
  2. 2022SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimationcited by 76
  3. 2021Unsupervised Depth Completion with Calibrated Backprojection Layerscited by 61
  4. 2021Learning Topology from Synthetic Data for Unsupervised Depth Completioncited by 38
  5. 2024WorDepth: Variational Language Prior for Monocular Depth Estimationcited by 34
  6. 2022Not Just Streaks: Towards Ground Truth for Single Image Derainingcited by 31
  7. 2021An Adaptive Framework for Learning Unsupervised Depth Completioncited by 30
  8. 2024Test-Time Adaptation for Depth Completioncited by 26
  9. 2022Monitored Distillation for Positive Congruent Depth Completioncited by 24
  10. 2024RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptionscited by 21
  11. 2021Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidancecited by 19
  12. 2023Monocular Visual-Inertial Depth Estimationcited by 18
21 more
  1. 2023Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalizationcited by 18
  2. 2021Stereoscopic Universal Perturbations across Different Architectures and Datasetscited by 17
  3. 2023Enhancing Diffusion Models with 3D Perspective Geometry Constraintscited by 16
  4. 2023AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimationcited by 15
  5. 2021Self-supervised 3D Representation Learning of Dressed Humans from Social Media Videoscited by 12
  6. 2025ProtoDepth: Unsupervised Continual Depth Completion with Prototypescited by 11
  7. 2022Struct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging Structural Regularities from Visual SLAMcited by 10
  8. 2025Extending Foundational Monocular Depth Estimators to Fisheye Cameras with Calibration Tokenscited by 9
  9. 2024All-day Depth Completioncited by 8
  10. 2022Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imagingcited by 6
  11. 2025ETA: Energy-based Test-time Adaptation for Depth Completioncited by 4
  12. 2022U-HRNet: Delving into Improving Semantic Representation of High Resolution Network for Dense Predictioncited by 3
  13. 2022PIFu for the Real World: A Self-supervised Framework to Reconstruct Dressed Human from Single-view Imagescited by 2
  14. 2024Deep Cost Ray Fusion for Sparse Depth Video Completioncited by 1
  15. 2026Triangular Consistency as a Universal Constraint for Learning Optical Flowcited by 1
  16. 2026VisTa3D: A Dataset and Benchmark for Thin Object Reconstruction from Vision, Tactile, and 3D Point Cloudscited by 1
  17. 2022On the Viability of Monocular Depth Pre-training for Semantic Segmentationcited by 0
  18. 2023DEUX: Active Exploration for Learning Unsupervised Depth Perceptioncited by 0
  19. 2023Self-Supervised Depth Completion Guided by 3D Perception and Geometry Consistencycited by 0
  20. 2025VIMD: Monocular Visual-Inertial Motion and Depth Estimationcited by 0
  21. 2026From Perspective to Fisheye Depth Estimation and Open-Vocabulary Segmentationcited by 0